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authorNick White <git@njw.name>2019-01-29 13:33:50 +0000
committerNick White <git@njw.name>2019-01-29 13:33:50 +0000
commit26a61941cf0216202aee3378f17b05255170da17 (patch)
treed2f3b554437d59dea5a6490491638d44dee6c241 /binarize/sauvola.go
parent0f145c2315f794210160d2c65874aaf051c5911b (diff)
Switch binarization to Sauvola algorithm
Diffstat (limited to 'binarize/sauvola.go')
-rw-r--r--binarize/sauvola.go91
1 files changed, 91 insertions, 0 deletions
diff --git a/binarize/sauvola.go b/binarize/sauvola.go
new file mode 100644
index 0000000..f1d0512
--- /dev/null
+++ b/binarize/sauvola.go
@@ -0,0 +1,91 @@
+package main
+
+import (
+ "image"
+ "image/color"
+ "math"
+)
+
+func mean(i []int) float64 {
+ sum := 0
+ for _, n := range i {
+ sum += n
+ }
+ return float64(sum) / float64(len(i))
+}
+
+// TODO: is there a prettier way of doing this than float64() all over the place?
+func stddev(i []int) float64 {
+ m := mean(i)
+
+ var sum float64
+ for _, n := range i {
+ sum += (float64(n) - m) * (float64(n) - m)
+ }
+ variance := float64(sum) / float64(len(i) - 1)
+ return math.Sqrt(variance)
+}
+
+func meanstddev(i []int) (float64, float64) {
+ m := mean(i)
+
+ var sum float64
+ for _, n := range i {
+ sum += (float64(n) - m) * (float64(n) - m)
+ }
+ variance := float64(sum) / float64(len(i) - 1)
+ return m, math.Sqrt(variance)
+}
+
+// gets the pixel values surrounding a point in the image
+func surrounding(img *image.Gray, x int, y int, size int) []int {
+ b := img.Bounds()
+
+ miny := y - size/2
+ if miny < b.Min.Y {
+ miny = b.Min.Y
+ }
+ minx := x - size/2
+ if minx < b.Min.X {
+ minx = b.Min.X
+ }
+ maxy := y + size/2
+ if maxy > b.Max.Y {
+ maxy = b.Max.Y
+ }
+ maxx := x + size/2
+ if maxx > b.Max.X {
+ maxx = b.Max.X
+ }
+
+ var s []int
+ for yi := miny; yi < maxy; yi++ {
+ for xi := minx; xi < maxx; xi++ {
+ s = append(s, int(img.GrayAt(xi, yi).Y))
+ }
+ }
+ return s
+}
+
+// TODO: parallelize
+// TODO: switch to using integral images to make faster; see paper
+// "Efficient Implementation of Local Adaptive Thresholding Techniques Using Integral Images"
+func Sauvola(img *image.Gray, ksize float64, windowsize int) *image.Gray {
+ b := img.Bounds()
+ new := image.NewGray(b)
+
+ for y := b.Min.Y; y < b.Max.Y; y++ {
+ for x := b.Min.X; x < b.Max.X; x++ {
+ window := surrounding(img, x, y, windowsize)
+ m, dev := meanstddev(window)
+ threshold := m * (1 + ksize * ((dev / 128) - 1))
+ if img.GrayAt(x, y).Y < uint8(threshold) {
+ new.SetGray(x, y, color.Gray{0})
+ } else {
+ new.SetGray(x, y, color.Gray{255})
+ }
+ }
+ }
+
+ return new
+}